Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations6019
Missing cells5311
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory658.5 KiB
Average record size in memory112.0 B

Variable types

Numeric5
Text5
Categorical4

Alerts

Kilometers_Driven is highly overall correlated with YearHigh correlation
Price is highly overall correlated with TransmissionHigh correlation
Transmission is highly overall correlated with PriceHigh correlation
Year is highly overall correlated with Kilometers_DrivenHigh correlation
Fuel_Type is highly imbalanced (53.4%)Imbalance
Owner_Type is highly imbalanced (60.9%)Imbalance
New_Price has 5195 (86.3%) missing valuesMissing
Kilometers_Driven is highly skewed (γ1 = 58.72466189)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique

Reproduction

Analysis started2025-09-24 12:34:09.870277
Analysis finished2025-09-24 12:34:13.059710
Duration3.19 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Uniform  Unique 

Distinct6019
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3009
Minimum0
Maximum6018
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-24T12:34:13.112341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile300.9
Q11504.5
median3009
Q34513.5
95-th percentile5717.1
Maximum6018
Range6018
Interquartile range (IQR)3009

Descriptive statistics

Standard deviation1737.68
Coefficient of variation (CV)0.57749417
Kurtosis-1.2
Mean3009
Median Absolute Deviation (MAD)1505
Skewness0
Sum18111171
Variance3019531.7
MonotonicityStrictly increasing
2025-09-24T12:34:13.199358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60181
 
< 0.1%
01
 
< 0.1%
11
 
< 0.1%
59791
 
< 0.1%
59801
 
< 0.1%
59811
 
< 0.1%
59821
 
< 0.1%
59831
 
< 0.1%
59841
 
< 0.1%
59851
 
< 0.1%
Other values (6009)6009
99.8%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
60181
< 0.1%
60171
< 0.1%
60161
< 0.1%
60151
< 0.1%
60141
< 0.1%
60131
< 0.1%
60121
< 0.1%
60111
< 0.1%
60101
< 0.1%
60091
< 0.1%

Name
Text

Distinct1876
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
2025-09-24T12:34:13.454742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length48
Mean length26.161987
Min length11

Characters and Unicode

Total characters157469
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique842 ?
Unique (%)14.0%

Sample

1st rowMaruti Wagon R LXI CNG
2nd rowHyundai Creta 1.6 CRDi SX Option
3rd rowHonda Jazz V
4th rowMaruti Ertiga VDI
5th rowAudi A4 New 2.0 TDI Multitronic
ValueCountFrequency (%)
maruti1211
 
4.2%
hyundai1107
 
3.8%
honda608
 
2.1%
at548
 
1.9%
diesel506
 
1.7%
1.2419
 
1.4%
toyota411
 
1.4%
tdi392
 
1.3%
swift353
 
1.2%
mt339
 
1.2%
Other values (862)23226
79.8%
2025-09-24T12:34:13.791494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23101
 
14.7%
a9788
 
6.2%
i9460
 
6.0%
e7731
 
4.9%
t6582
 
4.2%
o6572
 
4.2%
n6501
 
4.1%
r6372
 
4.0%
u4347
 
2.8%
d4099
 
2.6%
Other values (59)72916
46.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)157469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23101
 
14.7%
a9788
 
6.2%
i9460
 
6.0%
e7731
 
4.9%
t6582
 
4.2%
o6572
 
4.2%
n6501
 
4.1%
r6372
 
4.0%
u4347
 
2.8%
d4099
 
2.6%
Other values (59)72916
46.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)157469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23101
 
14.7%
a9788
 
6.2%
i9460
 
6.0%
e7731
 
4.9%
t6582
 
4.2%
o6572
 
4.2%
n6501
 
4.1%
r6372
 
4.0%
u4347
 
2.8%
d4099
 
2.6%
Other values (59)72916
46.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)157469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23101
 
14.7%
a9788
 
6.2%
i9460
 
6.0%
e7731
 
4.9%
t6582
 
4.2%
o6572
 
4.2%
n6501
 
4.1%
r6372
 
4.0%
u4347
 
2.8%
d4099
 
2.6%
Other values (59)72916
46.3%

Location
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Mumbai
790 
Hyderabad
742 
Kochi
651 
Coimbatore
636 
Pune
622 
Other values (6)
2578 

Length

Max length10
Median length7
Mean length6.8466523
Min length4

Characters and Unicode

Total characters41210
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowPune
3rd rowChennai
4th rowChennai
5th rowCoimbatore

Common Values

ValueCountFrequency (%)
Mumbai790
13.1%
Hyderabad742
12.3%
Kochi651
10.8%
Coimbatore636
10.6%
Pune622
10.3%
Delhi554
9.2%
Kolkata535
8.9%
Chennai494
8.2%
Jaipur413
6.9%
Bangalore358
5.9%

Length

2025-09-24T12:34:13.889898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai790
13.1%
hyderabad742
12.3%
kochi651
10.8%
coimbatore636
10.6%
pune622
10.3%
delhi554
9.2%
kolkata535
8.9%
chennai494
8.2%
jaipur413
6.9%
bangalore358
5.9%

Most occurring characters

ValueCountFrequency (%)
a6051
14.7%
e3630
 
8.8%
i3538
 
8.6%
o2816
 
6.8%
b2392
 
5.8%
r2149
 
5.2%
n1968
 
4.8%
d1932
 
4.7%
h1923
 
4.7%
u1825
 
4.4%
Other values (17)12986
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)41210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a6051
14.7%
e3630
 
8.8%
i3538
 
8.6%
o2816
 
6.8%
b2392
 
5.8%
r2149
 
5.2%
n1968
 
4.8%
d1932
 
4.7%
h1923
 
4.7%
u1825
 
4.4%
Other values (17)12986
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a6051
14.7%
e3630
 
8.8%
i3538
 
8.6%
o2816
 
6.8%
b2392
 
5.8%
r2149
 
5.2%
n1968
 
4.8%
d1932
 
4.7%
h1923
 
4.7%
u1825
 
4.4%
Other values (17)12986
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a6051
14.7%
e3630
 
8.8%
i3538
 
8.6%
o2816
 
6.8%
b2392
 
5.8%
r2149
 
5.2%
n1968
 
4.8%
d1932
 
4.7%
h1923
 
4.7%
u1825
 
4.4%
Other values (17)12986
31.5%

Year
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.3582
Minimum1998
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-24T12:34:13.955459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile2007
Q12011
median2014
Q32016
95-th percentile2018
Maximum2019
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2697421
Coefficient of variation (CV)0.001624024
Kurtosis0.89420088
Mean2013.3582
Median Absolute Deviation (MAD)2
Skewness-0.84580214
Sum12118403
Variance10.691214
MonotonicityNot monotonic
2025-09-24T12:34:14.015095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2014797
13.2%
2015744
12.4%
2016741
12.3%
2013649
10.8%
2017587
9.8%
2012580
9.6%
2011466
7.7%
2010342
5.7%
2018298
 
5.0%
2009198
 
3.3%
Other values (12)617
10.3%
ValueCountFrequency (%)
19984
 
0.1%
19992
 
< 0.1%
20004
 
0.1%
20018
 
0.1%
200215
 
0.2%
200317
 
0.3%
200431
 
0.5%
200557
0.9%
200678
1.3%
2007125
2.1%
ValueCountFrequency (%)
2019102
 
1.7%
2018298
 
5.0%
2017587
9.8%
2016741
12.3%
2015744
12.4%
2014797
13.2%
2013649
10.8%
2012580
9.6%
2011466
7.7%
2010342
5.7%

Kilometers_Driven
Real number (ℝ)

High correlation  Skewed 

Distinct3093
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58738.38
Minimum171
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-24T12:34:14.085077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile13007.4
Q134000
median53000
Q373000
95-th percentile120000
Maximum6500000
Range6499829
Interquartile range (IQR)39000

Descriptive statistics

Standard deviation91268.843
Coefficient of variation (CV)1.5538195
Kurtosis4125.0941
Mean58738.38
Median Absolute Deviation (MAD)19483
Skewness58.724662
Sum3.5354631 × 108
Variance8.3300017 × 109
MonotonicityNot monotonic
2025-09-24T12:34:14.189974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000082
 
1.4%
4500070
 
1.2%
6500068
 
1.1%
5000061
 
1.0%
5500060
 
1.0%
7000060
 
1.0%
3000054
 
0.9%
5200054
 
0.9%
8000050
 
0.8%
7500050
 
0.8%
Other values (3083)5410
89.9%
ValueCountFrequency (%)
1711
 
< 0.1%
6001
 
< 0.1%
10009
0.1%
10012
 
< 0.1%
10111
 
< 0.1%
10481
 
< 0.1%
12611
 
< 0.1%
13311
 
< 0.1%
14001
 
< 0.1%
16171
 
< 0.1%
ValueCountFrequency (%)
65000001
< 0.1%
7750001
< 0.1%
7200001
< 0.1%
6200001
< 0.1%
4800002
< 0.1%
4450001
< 0.1%
3000001
< 0.1%
2993221
< 0.1%
2820001
< 0.1%
2620001
< 0.1%

Fuel_Type
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Diesel
3205 
Petrol
2746 
CNG
 
56
LPG
 
10
Electric
 
2

Length

Max length8
Median length6
Mean length5.9677687
Min length3

Characters and Unicode

Total characters35920
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNG
2nd rowDiesel
3rd rowPetrol
4th rowDiesel
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel3205
53.2%
Petrol2746
45.6%
CNG56
 
0.9%
LPG10
 
0.2%
Electric2
 
< 0.1%

Length

2025-09-24T12:34:14.272408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T12:34:14.331859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel3205
53.2%
petrol2746
45.6%
cng56
 
0.9%
lpg10
 
0.2%
electric2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e9158
25.5%
l5953
16.6%
i3207
 
8.9%
D3205
 
8.9%
s3205
 
8.9%
P2756
 
7.7%
t2748
 
7.7%
r2748
 
7.7%
o2746
 
7.6%
G66
 
0.2%
Other values (5)128
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)35920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e9158
25.5%
l5953
16.6%
i3207
 
8.9%
D3205
 
8.9%
s3205
 
8.9%
P2756
 
7.7%
t2748
 
7.7%
r2748
 
7.7%
o2746
 
7.6%
G66
 
0.2%
Other values (5)128
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)35920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e9158
25.5%
l5953
16.6%
i3207
 
8.9%
D3205
 
8.9%
s3205
 
8.9%
P2756
 
7.7%
t2748
 
7.7%
r2748
 
7.7%
o2746
 
7.6%
G66
 
0.2%
Other values (5)128
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)35920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e9158
25.5%
l5953
16.6%
i3207
 
8.9%
D3205
 
8.9%
s3205
 
8.9%
P2756
 
7.7%
t2748
 
7.7%
r2748
 
7.7%
o2746
 
7.6%
G66
 
0.2%
Other values (5)128
 
0.4%

Transmission
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Manual
4299 
Automatic
1720 

Length

Max length9
Median length6
Mean length6.8572853
Min length6

Characters and Unicode

Total characters41274
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Manual4299
71.4%
Automatic1720
28.6%

Length

2025-09-24T12:34:14.427757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T12:34:14.492859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual4299
71.4%
automatic1720
28.6%

Most occurring characters

ValueCountFrequency (%)
a10318
25.0%
u6019
14.6%
M4299
10.4%
n4299
10.4%
l4299
10.4%
t3440
 
8.3%
A1720
 
4.2%
o1720
 
4.2%
m1720
 
4.2%
i1720
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)41274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a10318
25.0%
u6019
14.6%
M4299
10.4%
n4299
10.4%
l4299
10.4%
t3440
 
8.3%
A1720
 
4.2%
o1720
 
4.2%
m1720
 
4.2%
i1720
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a10318
25.0%
u6019
14.6%
M4299
10.4%
n4299
10.4%
l4299
10.4%
t3440
 
8.3%
A1720
 
4.2%
o1720
 
4.2%
m1720
 
4.2%
i1720
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a10318
25.0%
u6019
14.6%
M4299
10.4%
n4299
10.4%
l4299
10.4%
t3440
 
8.3%
A1720
 
4.2%
o1720
 
4.2%
m1720
 
4.2%
i1720
 
4.2%

Owner_Type
Categorical

Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
First
4929 
Second
968 
Third
 
113
Fourth & Above
 
9

Length

Max length14
Median length5
Mean length5.1742814
Min length5

Characters and Unicode

Total characters31144
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst
2nd rowFirst
3rd rowFirst
4th rowFirst
5th rowSecond

Common Values

ValueCountFrequency (%)
First4929
81.9%
Second968
 
16.1%
Third113
 
1.9%
Fourth & Above9
 
0.1%

Length

2025-09-24T12:34:14.549533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-24T12:34:14.592948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
first4929
81.6%
second968
 
16.0%
third113
 
1.9%
fourth9
 
0.1%
9
 
0.1%
above9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r5051
16.2%
i5042
16.2%
F4938
15.9%
t4938
15.9%
s4929
15.8%
d1081
 
3.5%
o986
 
3.2%
e977
 
3.1%
S968
 
3.1%
c968
 
3.1%
Other values (9)1266
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)31144
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r5051
16.2%
i5042
16.2%
F4938
15.9%
t4938
15.9%
s4929
15.8%
d1081
 
3.5%
o986
 
3.2%
e977
 
3.1%
S968
 
3.1%
c968
 
3.1%
Other values (9)1266
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31144
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r5051
16.2%
i5042
16.2%
F4938
15.9%
t4938
15.9%
s4929
15.8%
d1081
 
3.5%
o986
 
3.2%
e977
 
3.1%
S968
 
3.1%
c968
 
3.1%
Other values (9)1266
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31144
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r5051
16.2%
i5042
16.2%
F4938
15.9%
t4938
15.9%
s4929
15.8%
d1081
 
3.5%
o986
 
3.2%
e977
 
3.1%
S968
 
3.1%
c968
 
3.1%
Other values (9)1266
 
4.1%

Mileage
Text

Distinct442
Distinct (%)7.3%
Missing2
Missing (%)< 0.1%
Memory size47.2 KiB
2025-09-24T12:34:14.834637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9.3993685
Min length8

Characters and Unicode

Total characters56556
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)1.2%

Sample

1st row26.6 km/kg
2nd row19.67 kmpl
3rd row18.2 kmpl
4th row20.77 kmpl
5th row15.2 kmpl
ValueCountFrequency (%)
kmpl5951
49.5%
17.0173
 
1.4%
18.9172
 
1.4%
18.6119
 
1.0%
20.3688
 
0.7%
21.187
 
0.7%
17.885
 
0.7%
16.076
 
0.6%
12.872
 
0.6%
20.071
 
0.6%
Other values (422)5140
42.7%
2025-09-24T12:34:15.186031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
k6083
10.8%
.6017
10.6%
6017
10.6%
m6017
10.6%
l5951
10.5%
p5951
10.5%
15285
9.3%
23272
 
5.8%
01860
 
3.3%
71658
 
2.9%
Other values (8)8445
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)56556
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k6083
10.8%
.6017
10.6%
6017
10.6%
m6017
10.6%
l5951
10.5%
p5951
10.5%
15285
9.3%
23272
 
5.8%
01860
 
3.3%
71658
 
2.9%
Other values (8)8445
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)56556
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k6083
10.8%
.6017
10.6%
6017
10.6%
m6017
10.6%
l5951
10.5%
p5951
10.5%
15285
9.3%
23272
 
5.8%
01860
 
3.3%
71658
 
2.9%
Other values (8)8445
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)56556
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k6083
10.8%
.6017
10.6%
6017
10.6%
m6017
10.6%
l5951
10.5%
p5951
10.5%
15285
9.3%
23272
 
5.8%
01860
 
3.3%
71658
 
2.9%
Other values (8)8445
14.9%

Engine
Text

Distinct146
Distinct (%)2.4%
Missing36
Missing (%)0.6%
Memory size47.2 KiB
2025-09-24T12:34:15.368930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9015544
Min length5

Characters and Unicode

Total characters41292
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.5%

Sample

1st row998 CC
2nd row1582 CC
3rd row1199 CC
4th row1248 CC
5th row1968 CC
ValueCountFrequency (%)
cc5983
50.0%
1197606
 
5.1%
1248512
 
4.3%
1498304
 
2.5%
998259
 
2.2%
2179240
 
2.0%
1497229
 
1.9%
1198227
 
1.9%
1968216
 
1.8%
1995183
 
1.5%
Other values (137)3207
26.8%
2025-09-24T12:34:15.610465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C11966
29.0%
16169
14.9%
5983
14.5%
95477
13.3%
82498
 
6.0%
42111
 
5.1%
22055
 
5.0%
71714
 
4.2%
61212
 
2.9%
3943
 
2.3%
Other values (2)1164
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)41292
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C11966
29.0%
16169
14.9%
5983
14.5%
95477
13.3%
82498
 
6.0%
42111
 
5.1%
22055
 
5.0%
71714
 
4.2%
61212
 
2.9%
3943
 
2.3%
Other values (2)1164
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41292
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C11966
29.0%
16169
14.9%
5983
14.5%
95477
13.3%
82498
 
6.0%
42111
 
5.1%
22055
 
5.0%
71714
 
4.2%
61212
 
2.9%
3943
 
2.3%
Other values (2)1164
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41292
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C11966
29.0%
16169
14.9%
5983
14.5%
95477
13.3%
82498
 
6.0%
42111
 
5.1%
22055
 
5.0%
71714
 
4.2%
61212
 
2.9%
3943
 
2.3%
Other values (2)1164
 
2.8%

Power
Text

Distinct372
Distinct (%)6.2%
Missing36
Missing (%)0.6%
Memory size47.2 KiB
2025-09-24T12:34:15.888334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.9326425
Min length6

Characters and Unicode

Total characters47461
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)1.2%

Sample

1st row58.16 bhp
2nd row126.2 bhp
3rd row88.7 bhp
4th row88.76 bhp
5th row140.8 bhp
ValueCountFrequency (%)
bhp5983
50.0%
74235
 
2.0%
98.6131
 
1.1%
73.9125
 
1.0%
140123
 
1.0%
78.9111
 
0.9%
67.04107
 
0.9%
null107
 
0.9%
67.1107
 
0.9%
82101
 
0.8%
Other values (363)4836
40.4%
2025-09-24T12:34:16.219194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b5983
12.6%
5983
12.6%
h5983
12.6%
p5983
12.6%
.3693
7.8%
13663
7.7%
83195
6.7%
72316
 
4.9%
31771
 
3.7%
01721
 
3.6%
Other values (8)7170
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)47461
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b5983
12.6%
5983
12.6%
h5983
12.6%
p5983
12.6%
.3693
7.8%
13663
7.7%
83195
6.7%
72316
 
4.9%
31771
 
3.7%
01721
 
3.6%
Other values (8)7170
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)47461
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b5983
12.6%
5983
12.6%
h5983
12.6%
p5983
12.6%
.3693
7.8%
13663
7.7%
83195
6.7%
72316
 
4.9%
31771
 
3.7%
01721
 
3.6%
Other values (8)7170
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)47461
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b5983
12.6%
5983
12.6%
h5983
12.6%
p5983
12.6%
.3693
7.8%
13663
7.7%
83195
6.7%
72316
 
4.9%
31771
 
3.7%
01721
 
3.6%
Other values (8)7170
15.1%

Seats
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing42
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean5.2787352
Minimum0
Maximum10
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-24T12:34:16.284051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80883955
Coefficient of variation (CV)0.15322602
Kurtosis4.5333633
Mean5.2787352
Median Absolute Deviation (MAD)0
Skewness1.8357921
Sum31551
Variance0.65422143
MonotonicityNot monotonic
2025-09-24T12:34:16.335597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
55014
83.3%
7674
 
11.2%
8134
 
2.2%
499
 
1.6%
631
 
0.5%
216
 
0.3%
105
 
0.1%
93
 
< 0.1%
01
 
< 0.1%
(Missing)42
 
0.7%
ValueCountFrequency (%)
01
 
< 0.1%
216
 
0.3%
499
 
1.6%
55014
83.3%
631
 
0.5%
7674
 
11.2%
8134
 
2.2%
93
 
< 0.1%
105
 
0.1%
ValueCountFrequency (%)
105
 
0.1%
93
 
< 0.1%
8134
 
2.2%
7674
 
11.2%
631
 
0.5%
55014
83.3%
499
 
1.6%
216
 
0.3%
01
 
< 0.1%

New_Price
Text

Missing 

Distinct540
Distinct (%)65.5%
Missing5195
Missing (%)86.3%
Memory size47.2 KiB
2025-09-24T12:34:16.579809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.4332524
Min length4

Characters and Unicode

Total characters7773
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique349 ?
Unique (%)42.4%

Sample

1st row8.61 Lakh
2nd row21 Lakh
3rd row10.65 Lakh
4th row32.01 Lakh
5th row47.87 Lakh
ValueCountFrequency (%)
lakh807
49.0%
cr17
 
1.0%
4.786
 
0.4%
63.716
 
0.4%
95.136
 
0.4%
4.985
 
0.3%
44.285
 
0.3%
47.875
 
0.3%
11.265
 
0.3%
11.675
 
0.3%
Other values (532)781
47.4%
2025-09-24T12:34:16.898916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
824
10.6%
.810
10.4%
k807
10.4%
h807
10.4%
a807
10.4%
L807
10.4%
1518
 
6.7%
4318
 
4.1%
7297
 
3.8%
5294
 
3.8%
Other values (8)1484
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)7773
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
824
10.6%
.810
10.4%
k807
10.4%
h807
10.4%
a807
10.4%
L807
10.4%
1518
 
6.7%
4318
 
4.1%
7297
 
3.8%
5294
 
3.8%
Other values (8)1484
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7773
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
824
10.6%
.810
10.4%
k807
10.4%
h807
10.4%
a807
10.4%
L807
10.4%
1518
 
6.7%
4318
 
4.1%
7297
 
3.8%
5294
 
3.8%
Other values (8)1484
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7773
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
824
10.6%
.810
10.4%
k807
10.4%
h807
10.4%
a807
10.4%
L807
10.4%
1518
 
6.7%
4318
 
4.1%
7297
 
3.8%
5294
 
3.8%
Other values (8)1484
19.1%

Price
Real number (ℝ)

High correlation 

Distinct1373
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4794684
Minimum0.44
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2025-09-24T12:34:16.979769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.44
5-th percentile1.7
Q13.5
median5.64
Q39.95
95-th percentile32.446
Maximum160
Range159.56
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation11.187917
Coefficient of variation (CV)1.1802262
Kurtosis17.092202
Mean9.4794684
Median Absolute Deviation (MAD)2.62
Skewness3.335232
Sum57056.92
Variance125.16949
MonotonicityNot monotonic
2025-09-24T12:34:17.059568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.588
 
1.5%
5.584
 
1.4%
3.582
 
1.4%
4.2573
 
1.2%
3.2571
 
1.2%
368
 
1.1%
6.564
 
1.1%
2.563
 
1.0%
456
 
0.9%
4.7553
 
0.9%
Other values (1363)5317
88.3%
ValueCountFrequency (%)
0.441
 
< 0.1%
0.453
< 0.1%
0.52
< 0.1%
0.511
 
< 0.1%
0.532
< 0.1%
0.553
< 0.1%
0.62
< 0.1%
0.631
 
< 0.1%
0.652
< 0.1%
0.691
 
< 0.1%
ValueCountFrequency (%)
1601
< 0.1%
1201
< 0.1%
1001
< 0.1%
97.071
< 0.1%
93.671
< 0.1%
931
< 0.1%
901
< 0.1%
851
< 0.1%
83.961
< 0.1%
792
< 0.1%

Interactions

2025-09-24T12:34:11.871588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.296166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.614676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.058740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.471332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.962080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.359105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.712419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.138612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.533446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:12.050422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.421357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.778373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.222547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.622766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:12.302954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.485226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.846293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.312948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.700518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:12.380363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.547798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:10.963475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.391178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-24T12:34:11.781252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-24T12:34:17.123218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Fuel_TypeKilometers_DrivenLocationOwner_TypePriceSeatsTransmissionUnnamed: 0Year
Fuel_Type1.0000.0000.0950.0160.1480.1580.1520.0040.099
Kilometers_Driven0.0001.0000.0090.000-0.2150.1960.013-0.003-0.553
Location0.0950.0091.0000.1610.0740.0200.1840.0000.139
Owner_Type0.0160.0000.1611.0000.0580.0350.0130.0000.250
Price0.148-0.2150.0740.0581.0000.2210.595-0.0070.491
Seats0.1580.1960.0200.0350.2211.0000.147-0.0110.035
Transmission0.1520.0130.1840.0130.5950.1471.0000.0230.096
Unnamed: 00.004-0.0030.0000.000-0.007-0.0110.0231.0000.009
Year0.099-0.5530.1390.2500.4910.0350.0960.0091.000

Missing values

2025-09-24T12:34:12.689127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-24T12:34:12.849668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-24T12:34:13.002210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeMileageEnginePowerSeatsNew_PricePrice
00Maruti Wagon R LXI CNGMumbai201072000CNGManualFirst26.6 km/kg998 CC58.16 bhp5.0NaN1.8
11Hyundai Creta 1.6 CRDi SX OptionPune201541000DieselManualFirst19.67 kmpl1582 CC126.2 bhp5.0NaN12.5
22Honda Jazz VChennai201146000PetrolManualFirst18.2 kmpl1199 CC88.7 bhp5.08.61 Lakh4.5
33Maruti Ertiga VDIChennai201287000DieselManualFirst20.77 kmpl1248 CC88.76 bhp7.0NaN6.0
44Audi A4 New 2.0 TDI MultitronicCoimbatore201340670DieselAutomaticSecond15.2 kmpl1968 CC140.8 bhp5.0NaN17.7
55Hyundai EON LPG Era Plus OptionHyderabad201275000LPGManualFirst21.1 km/kg814 CC55.2 bhp5.0NaN2.4
66Nissan Micra Diesel XVJaipur201386999DieselManualFirst23.08 kmpl1461 CC63.1 bhp5.0NaN3.5
77Toyota Innova Crysta 2.8 GX AT 8SMumbai201636000DieselAutomaticFirst11.36 kmpl2755 CC171.5 bhp8.021 Lakh17.5
88Volkswagen Vento Diesel ComfortlinePune201364430DieselManualFirst20.54 kmpl1598 CC103.6 bhp5.0NaN5.2
99Tata Indica Vista Quadrajet LSChennai201265932DieselManualSecond22.3 kmpl1248 CC74 bhp5.0NaN1.9
Unnamed: 0NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeMileageEnginePowerSeatsNew_PricePrice
60096009Toyota Camry HybridMumbai201533500PetrolAutomaticFirst19.16 kmpl2494 CC158.2 bhp5.0NaN19.8
60106010Honda Brio 1.2 VX MTDelhi201333746PetrolManualFirst18.5 kmpl1198 CC86.8 bhp5.06.63 Lakh3.2
60116011Skoda Superb 3.6 V6 FSIHyderabad200953000PetrolAutomaticFirst0.0 kmpl3597 CC262.6 bhp5.0NaN4.8
60126012Toyota Innova 2.5 V Diesel 7-seaterCoimbatore201145004DieselManualFirst12.8 kmpl2494 CC102 bhp7.0NaN9.5
60136013Honda Amaze VX i-DTECCoimbatore201570602DieselManualFirst25.8 kmpl1498 CC98.6 bhp5.0NaN4.8
60146014Maruti Swift VDIDelhi201427365DieselManualFirst28.4 kmpl1248 CC74 bhp5.07.88 Lakh4.8
60156015Hyundai Xcent 1.1 CRDi SJaipur2015100000DieselManualFirst24.4 kmpl1120 CC71 bhp5.0NaN4.0
60166016Mahindra Xylo D4 BSIVJaipur201255000DieselManualSecond14.0 kmpl2498 CC112 bhp8.0NaN2.9
60176017Maruti Wagon R VXIKolkata201346000PetrolManualFirst18.9 kmpl998 CC67.1 bhp5.0NaN2.6
60186018Chevrolet Beat DieselHyderabad201147000DieselManualFirst25.44 kmpl936 CC57.6 bhp5.0NaN2.5